Overview

Dataset statistics

Number of variables22
Number of observations1801
Missing cells10061
Missing cells (%)25.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory559.8 KiB
Average record size in memory318.3 B

Variable types

Categorical2
Numeric19
Unsupported1

Alerts

Country has a high cardinality: 147 distinct valuesHigh cardinality
Country_Code has a high cardinality: 147 distinct valuesHigh cardinality
Year is highly overall correlated with GDP_Per_Capita and 3 other fieldsHigh correlation
Gross_Production is highly overall correlated with GDP and 3 other fieldsHigh correlation
GDP is highly overall correlated with Gross_Production and 9 other fieldsHigh correlation
GDP_Per_Capita is highly overall correlated with Year and 8 other fieldsHigh correlation
No of frost days is highly overall correlated with Avg temperature and 3 other fieldsHigh correlation
Avg temperature is highly overall correlated with No of frost days and 4 other fieldsHigh correlation
Control of Corruption: Estimate is highly overall correlated with GDP_Per_Capita and 5 other fieldsHigh correlation
Use of IMF credit (DOD, current US$) is highly overall correlated with GDP and 1 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, both sexes (%) is highly overall correlated with Year and 9 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, female (%) is highly overall correlated with Year and 10 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, male (%) is highly overall correlated with Year and 10 other fieldsHigh correlation
Area is highly overall correlated with Gross_Production and 2 other fieldsHigh correlation
Population is highly overall correlated with Gross_Production and 3 other fieldsHigh correlation
Quality of trade- and transport-related infrastructure, score (1=low to 5=high) is highly overall correlated with GDP and 8 other fieldsHigh correlation
Gini coefficient is highly overall correlated with No of frost days and 1 other fieldsHigh correlation
Total Fertilizer Use is highly overall correlated with GDP and 7 other fieldsHigh correlation
Agriculture Research Spending is highly overall correlated with Gross_Production and 5 other fieldsHigh correlation
Gross_Prod_Per_Capita is highly overall correlated with GDP_Per_Capita and 5 other fieldsHigh correlation
GDP has 27 (1.5%) missing valuesMissing
GDP_Per_Capita has 27 (1.5%) missing valuesMissing
Control of Corruption: Estimate has 1059 (58.8%) missing valuesMissing
Use of IMF credit (DOD, current US$) has 352 (19.5%) missing valuesMissing
Gross enrolment ratio, primary to tertiary, female (%) has 473 (26.3%) missing valuesMissing
Gross enrolment ratio, primary to tertiary, male (%) has 473 (26.3%) missing valuesMissing
Quality of trade- and transport-related infrastructure, score (1=low to 5=high) has 1725 (95.8%) missing valuesMissing
Gini coefficient has 1393 (77.3%) missing valuesMissing
Total Fertilizer Use has 1284 (71.3%) missing valuesMissing
Agriculture Research Spending has 1562 (86.7%) missing valuesMissing
GHI has 1686 (93.6%) missing valuesMissing
Gross enrolment ratio, primary to tertiary, both sexes (%) has unique valuesUnique
Gross_Prod_Per_Capita has unique valuesUnique
GHI is an unsupported type, check if it needs cleaning or further analysisUnsupported
No of frost days has 988 (54.9%) zerosZeros
Use of IMF credit (DOD, current US$) has 242 (13.4%) zerosZeros
Total Fertilizer Use has 38 (2.1%) zerosZeros

Reproduction

Analysis started2023-10-24 18:57:55.401369
Analysis finished2023-10-24 18:58:48.619353
Duration53.22 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Country
Categorical

Distinct147
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size128.2 KiB
Tunisia
 
37
Peru
 
35
Indonesia
 
34
Morocco
 
34
Colombia
 
34
Other values (142)
1627 

Length

Max length24
Median length19
Mean length7.8661855
Min length4

Characters and Unicode

Total characters14167
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.5%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAlbania
4th rowAlbania
5th rowAlbania

Common Values

ValueCountFrequency (%)
Tunisia 37
 
2.1%
Peru 35
 
1.9%
Indonesia 34
 
1.9%
Morocco 34
 
1.9%
Colombia 34
 
1.9%
Malawi 32
 
1.8%
Lesotho 32
 
1.8%
Jordan 32
 
1.8%
Philippines 32
 
1.8%
Mauritius 30
 
1.7%
Other values (137) 1469
81.6%

Length

2023-10-24T11:58:48.731138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic 47
 
2.3%
tunisia 37
 
1.8%
peru 35
 
1.7%
indonesia 34
 
1.7%
morocco 34
 
1.7%
colombia 34
 
1.7%
malawi 32
 
1.6%
lesotho 32
 
1.6%
jordan 32
 
1.6%
philippines 32
 
1.6%
Other values (154) 1704
83.0%

Most occurring characters

ValueCountFrequency (%)
a 2203
15.6%
i 1386
 
9.8%
n 1130
 
8.0%
e 894
 
6.3%
o 844
 
6.0%
r 816
 
5.8%
l 610
 
4.3%
u 579
 
4.1%
s 464
 
3.3%
d 421
 
3.0%
Other values (41) 4820
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11855
83.7%
Uppercase Letter 2056
 
14.5%
Space Separator 252
 
1.8%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2203
18.6%
i 1386
11.7%
n 1130
9.5%
e 894
 
7.5%
o 844
 
7.1%
r 816
 
6.9%
l 610
 
5.1%
u 579
 
4.9%
s 464
 
3.9%
d 421
 
3.6%
Other values (16) 2508
21.2%
Uppercase Letter
ValueCountFrequency (%)
M 256
12.5%
C 181
 
8.8%
S 171
 
8.3%
B 161
 
7.8%
A 128
 
6.2%
P 124
 
6.0%
G 114
 
5.5%
I 111
 
5.4%
R 110
 
5.4%
T 109
 
5.3%
Other values (13) 591
28.7%
Space Separator
ValueCountFrequency (%)
252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13911
98.2%
Common 256
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2203
15.8%
i 1386
 
10.0%
n 1130
 
8.1%
e 894
 
6.4%
o 844
 
6.1%
r 816
 
5.9%
l 610
 
4.4%
u 579
 
4.2%
s 464
 
3.3%
d 421
 
3.0%
Other values (39) 4564
32.8%
Common
ValueCountFrequency (%)
252
98.4%
- 4
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2203
15.6%
i 1386
 
9.8%
n 1130
 
8.0%
e 894
 
6.3%
o 844
 
6.0%
r 816
 
5.8%
l 610
 
4.3%
u 579
 
4.1%
s 464
 
3.3%
d 421
 
3.0%
Other values (41) 4820
34.0%

Year
Real number (ℝ)

Distinct38
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.9106
Minimum1970
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.9 KiB
2023-10-24T11:58:48.872936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972
Q11982
median1994
Q32002
95-th percentile2006
Maximum2007
Range37
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.450439
Coefficient of variation (CV)0.0057484705
Kurtosis-1.2608184
Mean1991.9106
Median Absolute Deviation (MAD)10
Skewness-0.33795002
Sum3587431
Variance131.11256
MonotonicityNot monotonic
2023-10-24T11:58:48.988251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2005 94
 
5.2%
2006 91
 
5.1%
2004 90
 
5.0%
2007 89
 
4.9%
2003 86
 
4.8%
2002 86
 
4.8%
2000 84
 
4.7%
1996 69
 
3.8%
1998 56
 
3.1%
1999 51
 
2.8%
Other values (28) 1005
55.8%
ValueCountFrequency (%)
1970 15
 
0.8%
1971 41
2.3%
1972 35
1.9%
1973 38
2.1%
1974 36
2.0%
1975 38
2.1%
1976 40
2.2%
1977 39
2.2%
1978 37
2.1%
1979 40
2.2%
ValueCountFrequency (%)
2007 89
4.9%
2006 91
5.1%
2005 94
5.2%
2004 90
5.0%
2003 86
4.8%
2002 86
4.8%
2001 48
2.7%
2000 84
4.7%
1999 51
2.8%
1998 56
3.1%

Gross_Production
Real number (ℝ)

Distinct1799
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7813175.2
Minimum3151
Maximum3.864482 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:49.145962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3151
5-th percentile53825
Q1510608
median1327755
Q34945471
95-th percentile27352720
Maximum3.864482 × 108
Range3.8644505 × 108
Interquartile range (IQR)4434863

Descriptive statistics

Standard deviation25219620
Coefficient of variation (CV)3.2278324
Kurtosis87.249673
Mean7813175.2
Median Absolute Deviation (MAD)1158241
Skewness8.0765269
Sum1.4071529 × 1010
Variance6.3602924 × 1014
MonotonicityNot monotonic
2023-10-24T11:58:49.288799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3260 3
 
0.2%
2226346 1
 
0.1%
9341945 1
 
0.1%
512414 1
 
0.1%
526245 1
 
0.1%
567914 1
 
0.1%
816338 1
 
0.1%
6520025 1
 
0.1%
10580190 1
 
0.1%
9803666 1
 
0.1%
Other values (1789) 1789
99.3%
ValueCountFrequency (%)
3151 1
 
0.1%
3260 3
0.2%
3284 1
 
0.1%
5888 1
 
0.1%
5914 1
 
0.1%
5949 1
 
0.1%
6033 1
 
0.1%
6054 1
 
0.1%
6176 1
 
0.1%
6273 1
 
0.1%
ValueCountFrequency (%)
386448200 1
0.1%
380320200 1
0.1%
322771800 1
0.1%
271766600 1
0.1%
199926200 1
0.1%
192616000 1
0.1%
179084400 1
0.1%
175747700 1
0.1%
174290700 1
0.1%
169315100 1
0.1%

GDP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1774
Distinct (%)100.0%
Missing27
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean9.5419667 × 1010
Minimum76482103
Maximum3.55034 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:49.444850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum76482103
5-th percentile4.6258371 × 108
Q11.9327174 × 109
median7.1864388 × 109
Q34.3050096 × 1010
95-th percentile4.185521 × 1011
Maximum3.55034 × 1012
Range3.5502635 × 1012
Interquartile range (IQR)4.1117379 × 1010

Descriptive statistics

Standard deviation3.0082453 × 1011
Coefficient of variation (CV)3.152647
Kurtosis43.567069
Mean9.5419667 × 1010
Median Absolute Deviation (MAD)6.3608881 × 109
Skewness6.02812
Sum1.6927449 × 1014
Variance9.0495399 × 1022
MonotonicityNot monotonic
2023-10-24T11:58:49.579328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5226778809 1
 
0.1%
3105517091 1
 
0.1%
2885710609 1
 
0.1%
3851213728 1
 
0.1%
1009455484 1
 
0.1%
1488804124 1
 
0.1%
1792800000 1
 
0.1%
4140470000 1
 
0.1%
5224213018 1
 
0.1%
7.01408353 × 10101
 
0.1%
Other values (1764) 1764
97.9%
(Missing) 27
 
1.5%
ValueCountFrequency (%)
76482102.91 1
0.1%
80915831.92 1
0.1%
102244362.2 1
0.1%
102367039.3 1
0.1%
109157070.7 1
0.1%
110234939.8 1
0.1%
110900457 1
0.1%
111022090 1
0.1%
112133944.3 1
0.1%
121181556.2 1
0.1%
ValueCountFrequency (%)
3.55034 × 10121
0.1%
3.10618 × 10121
0.1%
2.75213 × 10121
0.1%
2.71706 × 10121
0.1%
2.66059 × 10121
0.1%
2.54483 × 10121
0.1%
2.49724 × 10121
0.1%
2.42181 × 10121
0.1%
2.32054 × 10121
0.1%
2.2131 × 10121
0.1%

GDP_Per_Capita
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1774
Distinct (%)100.0%
Missing27
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean5257.306
Minimum20.039529
Maximum130655.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:49.736212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20.039529
5-th percentile150.30498
Q1337.57613
median904.34843
Q33035.9299
95-th percentile31105.259
Maximum130655.64
Range130635.6
Interquartile range (IQR)2698.3538

Descriptive statistics

Standard deviation12225.374
Coefficient of variation (CV)2.3254066
Kurtosis22.763385
Mean5257.306
Median Absolute Deviation (MAD)680.23413
Skewness4.1567794
Sum9326460.8
Variance1.4945977 × 108
MonotonicityNot monotonic
2023-10-24T11:58:49.909908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211.3820742 1
 
0.1%
852.466606 1
 
0.1%
754.8025687 1
 
0.1%
985.0477809 1
 
0.1%
241.8764121 1
 
0.1%
348.8646053 1
 
0.1%
410.7673844 1
 
0.1%
890.0054491 1
 
0.1%
1000.936715 1
 
0.1%
18794.43604 1
 
0.1%
Other values (1764) 1764
97.9%
(Missing) 27
 
1.5%
ValueCountFrequency (%)
20.03952875 1
0.1%
22.20941504 1
0.1%
22.81397279 1
0.1%
27.46376418 1
0.1%
30.44526282 1
0.1%
31.23485178 1
0.1%
34.51653083 1
0.1%
34.73930543 1
0.1%
36.37603624 1
0.1%
36.45524107 1
0.1%
ValueCountFrequency (%)
130655.637 1
0.1%
114374.2465 1
0.1%
105399.2605 1
0.1%
100289.243 1
0.1%
90788.80049 1
0.1%
89859.86537 1
0.1%
85139.96045 1
0.1%
74148.32008 1
0.1%
69495.72674 1
0.1%
66810.47852 1
0.1%

Country_Code
Categorical

Distinct147
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size119.6 KiB
TUN
 
37
PER
 
35
IDN
 
34
MAR
 
34
COL
 
34
Other values (142)
1627 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5403
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.5%

Sample

1st rowAFG
2nd rowAFG
3rd rowALB
4th rowALB
5th rowALB

Common Values

ValueCountFrequency (%)
TUN 37
 
2.1%
PER 35
 
1.9%
IDN 34
 
1.9%
MAR 34
 
1.9%
COL 34
 
1.9%
MWI 32
 
1.8%
LSO 32
 
1.8%
JOR 32
 
1.8%
PHL 32
 
1.8%
MUS 30
 
1.7%
Other values (137) 1469
81.6%

Length

2023-10-24T11:58:50.040445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tun 37
 
2.1%
per 35
 
1.9%
idn 34
 
1.9%
mar 34
 
1.9%
col 34
 
1.9%
mwi 32
 
1.8%
lso 32
 
1.8%
jor 32
 
1.8%
phl 32
 
1.8%
bfa 30
 
1.7%
Other values (137) 1469
81.6%

Most occurring characters

ValueCountFrequency (%)
R 479
 
8.9%
M 392
 
7.3%
N 392
 
7.3%
A 383
 
7.1%
L 338
 
6.3%
E 289
 
5.3%
I 288
 
5.3%
G 270
 
5.0%
B 260
 
4.8%
S 258
 
4.8%
Other values (16) 2054
38.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5403
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 479
 
8.9%
M 392
 
7.3%
N 392
 
7.3%
A 383
 
7.1%
L 338
 
6.3%
E 289
 
5.3%
I 288
 
5.3%
G 270
 
5.0%
B 260
 
4.8%
S 258
 
4.8%
Other values (16) 2054
38.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5403
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 479
 
8.9%
M 392
 
7.3%
N 392
 
7.3%
A 383
 
7.1%
L 338
 
6.3%
E 289
 
5.3%
I 288
 
5.3%
G 270
 
5.0%
B 260
 
4.8%
S 258
 
4.8%
Other values (16) 2054
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 479
 
8.9%
M 392
 
7.3%
N 392
 
7.3%
A 383
 
7.1%
L 338
 
6.3%
E 289
 
5.3%
I 288
 
5.3%
G 270
 
5.0%
B 260
 
4.8%
S 258
 
4.8%
Other values (16) 2054
38.0%

No of frost days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct537
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4228706
Minimum0
Maximum19.79
Zeros988
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:50.150972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.57
95-th percentile13.3
Maximum19.79
Range19.79
Interquartile range (IQR)2.57

Descriptive statistics

Standard deviation4.5149615
Coefficient of variation (CV)1.8634761
Kurtosis3.2526603
Mean2.4228706
Median Absolute Deviation (MAD)0
Skewness2.0218838
Sum4363.59
Variance20.384878
MonotonicityNot monotonic
2023-10-24T11:58:50.293129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 988
54.9%
0.01 32
 
1.8%
0.02 14
 
0.8%
0.04 11
 
0.6%
0.2 8
 
0.4%
0.11 7
 
0.4%
0.1 6
 
0.3%
0.12 6
 
0.3%
0.06 6
 
0.3%
0.21 6
 
0.3%
Other values (527) 717
39.8%
ValueCountFrequency (%)
0 988
54.9%
0.01 32
 
1.8%
0.02 14
 
0.8%
0.03 4
 
0.2%
0.04 11
 
0.6%
0.05 4
 
0.2%
0.06 6
 
0.3%
0.07 6
 
0.3%
0.08 4
 
0.2%
0.09 2
 
0.1%
ValueCountFrequency (%)
19.79 1
0.1%
19.76 1
0.1%
19.66 1
0.1%
19.58 1
0.1%
19.54 1
0.1%
19.48 1
0.1%
19.47 1
0.1%
19.46 1
0.1%
19.42 1
0.1%
19.36 1
0.1%

Precipitation
Real number (ℝ)

Distinct1708
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.96175
Minimum2.64
Maximum369.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:50.434416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.64
5-th percentile8.98
Q153.56
median93.2
Q3150.4
95-th percentile259.89
Maximum369.49
Range366.85
Interquartile range (IQR)96.84

Descriptive statistics

Standard deviation73.76654
Coefficient of variation (CV)0.68965342
Kurtosis0.15799409
Mean106.96175
Median Absolute Deviation (MAD)48.33
Skewness0.79778006
Sum192638.12
Variance5441.5024
MonotonicityNot monotonic
2023-10-24T11:58:50.569177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.5 3
 
0.2%
21.39 3
 
0.2%
70.27 3
 
0.2%
16.51 3
 
0.2%
60.33 3
 
0.2%
142.52 3
 
0.2%
23.31 3
 
0.2%
125.72 3
 
0.2%
59.04 2
 
0.1%
20.92 2
 
0.1%
Other values (1698) 1773
98.4%
ValueCountFrequency (%)
2.64 1
0.1%
2.86 1
0.1%
3.02 1
0.1%
3.07 1
0.1%
3.3 1
0.1%
3.52 1
0.1%
3.57 1
0.1%
3.59 1
0.1%
3.72 1
0.1%
3.74 1
0.1%
ValueCountFrequency (%)
369.49 1
0.1%
359.57 1
0.1%
346.28 1
0.1%
343.35 1
0.1%
338.55 1
0.1%
331.14 1
0.1%
321.34 1
0.1%
320.97 1
0.1%
319.58 1
0.1%
317.44 1
0.1%

Avg temperature
Real number (ℝ)

Distinct1157
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.817912
Minimum-4.88
Maximum29.09
Zeros0
Zeros (%)0.0%
Negative20
Negative (%)1.1%
Memory size28.1 KiB
2023-10-24T11:58:50.710565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-4.88
5-th percentile5.84
Q115.22
median22.54
Q325.35
95-th percentile27.61
Maximum29.09
Range33.97
Interquartile range (IQR)10.13

Descriptive statistics

Standard deviation7.3339715
Coefficient of variation (CV)0.37006781
Kurtosis0.33377164
Mean19.817912
Median Absolute Deviation (MAD)3.51
Skewness-1.0891837
Sum35692.06
Variance53.787137
MonotonicityNot monotonic
2023-10-24T11:58:50.835982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.53 6
 
0.3%
25.86 6
 
0.3%
25.17 6
 
0.3%
24.76 6
 
0.3%
25.5 6
 
0.3%
25.01 6
 
0.3%
26.25 5
 
0.3%
24.9 5
 
0.3%
25.3 5
 
0.3%
25.07 5
 
0.3%
Other values (1147) 1745
96.9%
ValueCountFrequency (%)
-4.88 1
0.1%
-4.79 1
0.1%
-4.4 1
0.1%
-4.38 1
0.1%
-4.34 1
0.1%
-4.33 1
0.1%
-4.13 1
0.1%
-4.12 1
0.1%
-4.1 1
0.1%
-3.95 1
0.1%
ValueCountFrequency (%)
29.09 1
 
0.1%
29.05 1
 
0.1%
29 1
 
0.1%
28.94 1
 
0.1%
28.9 2
0.1%
28.84 1
 
0.1%
28.71 1
 
0.1%
28.68 3
0.2%
28.66 1
 
0.1%
28.62 2
0.1%

Control of Corruption: Estimate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct742
Distinct (%)100.0%
Missing1059
Missing (%)58.8%
Infinite0
Infinite (%)0.0%
Mean0.17360449
Minimum-1.6255742
Maximum2.4591184
Zeros0
Zeros (%)0.0%
Negative372
Negative (%)20.7%
Memory size28.1 KiB
2023-10-24T11:58:50.969684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.6255742
5-th percentile-1.1749837
Q1-0.59750335
median-0.013356876
Q30.88947529
95-th percentile2.1235431
Maximum2.4591184
Range4.0846926
Interquartile range (IQR)1.4869786

Descriptive statistics

Standard deviation1.0064264
Coefficient of variation (CV)5.7972368
Kurtosis-0.63352466
Mean0.17360449
Median Absolute Deviation (MAD)0.7055883
Skewness0.53037022
Sum128.81453
Variance1.012894
MonotonicityNot monotonic
2023-10-24T11:58:51.095455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.35064733 1
 
0.1%
2.110245705 1
 
0.1%
2.159472227 1
 
0.1%
2.040526152 1
 
0.1%
1.960848689 1
 
0.1%
1.975515485 1
 
0.1%
1.995902896 1
 
0.1%
2.068361998 1
 
0.1%
2.138006687 1
 
0.1%
2.089596987 1
 
0.1%
Other values (732) 732
40.6%
(Missing) 1059
58.8%
ValueCountFrequency (%)
-1.625574231 1
0.1%
-1.531993032 1
0.1%
-1.527264118 1
0.1%
-1.502067566 1
0.1%
-1.489415765 1
0.1%
-1.471516371 1
0.1%
-1.465013862 1
0.1%
-1.44834578 1
0.1%
-1.445619464 1
0.1%
-1.442982793 1
0.1%
ValueCountFrequency (%)
2.459118366 1
0.1%
2.454110861 1
0.1%
2.434834003 1
0.1%
2.410111427 1
0.1%
2.406824589 1
0.1%
2.389477491 1
0.1%
2.374123812 1
0.1%
2.369009733 1
0.1%
2.368235588 1
0.1%
2.321177483 1
0.1%

Use of IMF credit (DOD, current US$)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1199
Distinct (%)82.7%
Missing352
Missing (%)19.5%
Infinite0
Infinite (%)0.0%
Mean5.5621559 × 108
Minimum0
Maximum2.8850329 × 1010
Zeros242
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:51.826443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18590533.6
median65763748
Q32.6678719 × 108
95-th percentile2.0971474 × 109
Maximum2.8850329 × 1010
Range2.8850329 × 1010
Interquartile range (IQR)2.5819666 × 108

Descriptive statistics

Standard deviation2.1063488 × 109
Coefficient of variation (CV)3.7869287
Kurtosis68.594877
Mean5.5621559 × 108
Median Absolute Deviation (MAD)65763748
Skewness7.4995967
Sum8.0595639 × 1011
Variance4.4367053 × 1018
MonotonicityNot monotonic
2023-10-24T11:58:51.971488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 242
 
13.4%
12000 4
 
0.2%
16140313.6 2
 
0.1%
8957025 2
 
0.1%
14786947.8 2
 
0.1%
14013909.4 2
 
0.1%
7097774 2
 
0.1%
7019432.5 2
 
0.1%
91662085.2 1
 
0.1%
49184434.8 1
 
0.1%
Other values (1189) 1189
66.0%
(Missing) 352
 
19.5%
ValueCountFrequency (%)
0 242
13.4%
12000 4
 
0.2%
69710.3 1
 
0.1%
101008.9 1
 
0.1%
354358.9 1
 
0.1%
354900.6 1
 
0.1%
367941.8 1
 
0.1%
383928.4 1
 
0.1%
387596.8 1
 
0.1%
416000 1
 
0.1%
ValueCountFrequency (%)
2.885032896 × 10101
0.1%
2.558639107 × 10101
0.1%
2.302259808 × 10101
0.1%
2.131497601 × 10101
0.1%
1.933505715 × 10101
0.1%
1.599577607 × 10101
0.1%
1.582824523 × 10101
0.1%
1.477233495 × 10101
0.1%
1.458496762 × 10101
0.1%
1.437633142 × 10101
0.1%

Gross enrolment ratio, primary to tertiary, both sexes (%)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.755614
Minimum4.45329
Maximum113.51159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:52.113582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.45329
5-th percentile18.45332
Q142.27529
median61.21061
Q375.23842
95-th percentile93.34734
Maximum113.51159
Range109.0583
Interquartile range (IQR)32.96313

Descriptive statistics

Standard deviation22.610274
Coefficient of variation (CV)0.38481896
Kurtosis-0.56994158
Mean58.755614
Median Absolute Deviation (MAD)15.3975
Skewness-0.25063913
Sum105818.86
Variance511.22451
MonotonicityNot monotonic
2023-10-24T11:58:52.271653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.92417 1
 
0.1%
11.69478 1
 
0.1%
56.07063 1
 
0.1%
58.48752 1
 
0.1%
69.69496 1
 
0.1%
94.69988 1
 
0.1%
98.60754 1
 
0.1%
97.39507 1
 
0.1%
97.77402 1
 
0.1%
97.4608 1
 
0.1%
Other values (1791) 1791
99.4%
ValueCountFrequency (%)
4.45329 1
0.1%
5.20742 1
0.1%
5.22189 1
0.1%
5.31937 1
0.1%
5.47649 1
0.1%
5.55825 1
0.1%
5.73814 1
0.1%
5.75071 1
0.1%
6.03664 1
0.1%
6.17859 1
0.1%
ValueCountFrequency (%)
113.51159 1
0.1%
113.42767 1
0.1%
112.46801 1
0.1%
112.36465 1
0.1%
112.0835 1
0.1%
112.05495 1
0.1%
111.43046 1
0.1%
111.10589 1
0.1%
110.0657 1
0.1%
109.66457 1
0.1%

Gross enrolment ratio, primary to tertiary, female (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1328
Distinct (%)100.0%
Missing473
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean59.096475
Minimum2.38836
Maximum122.6919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:52.460260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.38836
5-th percentile11.929603
Q137.093595
median63.039645
Q378.77745
95-th percentile99.131642
Maximum122.6919
Range120.30354
Interquartile range (IQR)41.683855

Descriptive statistics

Standard deviation26.995701
Coefficient of variation (CV)0.45680731
Kurtosis-0.8476282
Mean59.096475
Median Absolute Deviation (MAD)19.133615
Skewness-0.22385769
Sum78480.118
Variance728.76789
MonotonicityNot monotonic
2023-10-24T11:58:52.586063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.09865 1
 
0.1%
34.02897 1
 
0.1%
52.17884 1
 
0.1%
55.94605 1
 
0.1%
60.09614 1
 
0.1%
72.79504 1
 
0.1%
73.8473 1
 
0.1%
73.70536 1
 
0.1%
71.98553 1
 
0.1%
71.47243 1
 
0.1%
Other values (1318) 1318
73.2%
(Missing) 473
 
26.3%
ValueCountFrequency (%)
2.38836 1
0.1%
3.52024 1
0.1%
3.69333 1
0.1%
3.83421 1
0.1%
3.90983 1
0.1%
4.05133 1
0.1%
4.2108 1
0.1%
4.26355 1
0.1%
4.44357 1
0.1%
4.56034 1
0.1%
ValueCountFrequency (%)
122.6919 1
0.1%
122.20878 1
0.1%
120.87788 1
0.1%
119.42114 1
0.1%
119.02966 1
0.1%
117.42246 1
0.1%
116.66313 1
0.1%
116.31898 1
0.1%
115.91076 1
0.1%
114.48593 1
0.1%

Gross enrolment ratio, primary to tertiary, male (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1328
Distinct (%)100.0%
Missing473
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean62.597012
Minimum6.76617
Maximum109.21123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:52.732884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.76617
5-th percentile23.802591
Q149.183345
median66.842065
Q376.5112
95-th percentile91.387642
Maximum109.21123
Range102.44506
Interquartile range (IQR)27.327855

Descriptive statistics

Standard deviation21.019311
Coefficient of variation (CV)0.33578777
Kurtosis-0.27597479
Mean62.597012
Median Absolute Deviation (MAD)12.813535
Skewness-0.52527143
Sum83128.832
Variance441.81145
MonotonicityNot monotonic
2023-10-24T11:58:52.858736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.26862 1
 
0.1%
42.16719 1
 
0.1%
66.68293 1
 
0.1%
66.95442 1
 
0.1%
68.82968 1
 
0.1%
71.96006 1
 
0.1%
72.76231 1
 
0.1%
73.42181 1
 
0.1%
72.09901 1
 
0.1%
71.14254 1
 
0.1%
Other values (1318) 1318
73.2%
(Missing) 473
 
26.3%
ValueCountFrequency (%)
6.76617 1
0.1%
6.9467 1
0.1%
7.03014 1
0.1%
7.23317 1
0.1%
7.28181 1
0.1%
7.59833 1
0.1%
7.61054 1
0.1%
7.91825 1
0.1%
8.07128 1
0.1%
8.10891 1
0.1%
ValueCountFrequency (%)
109.21123 1
0.1%
108.06169 1
0.1%
107.98403 1
0.1%
107.83812 1
0.1%
106.97398 1
0.1%
106.52531 1
0.1%
105.84017 1
0.1%
103.69312 1
0.1%
103.0023 1
0.1%
102.73885 1
0.1%

Area
Real number (ℝ)

Distinct1447
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27483.802
Minimum6
Maximum552832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:52.972965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile101
Q12237
median8585
Q325951
95-th percentile128405
Maximum552832
Range552826
Interquartile range (IQR)23714

Descriptive statistics

Standard deviation64057.226
Coefficient of variation (CV)2.3307265
Kurtosis31.742292
Mean27483.802
Median Absolute Deviation (MAD)7137
Skewness5.1480192
Sum49498327
Variance4.1033283 × 109
MonotonicityNot monotonic
2023-10-24T11:58:53.098358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114 12
 
0.7%
6 11
 
0.6%
3630 9
 
0.5%
10 8
 
0.4%
2281 8
 
0.4%
147 8
 
0.4%
71 8
 
0.4%
113 7
 
0.4%
2150 7
 
0.4%
11 7
 
0.4%
Other values (1437) 1716
95.3%
ValueCountFrequency (%)
6 11
0.6%
7 3
 
0.2%
9 5
0.3%
9.2 1
 
0.1%
9.3 2
 
0.1%
10 8
0.4%
10.3 1
 
0.1%
11 7
0.4%
11.4 1
 
0.1%
13 1
 
0.1%
ValueCountFrequency (%)
552832 1
0.1%
550536 1
0.1%
543356 1
0.1%
535080 1
0.1%
531398 1
0.1%
531392 1
0.1%
531003 1
0.1%
522704 1
0.1%
513795 1
0.1%
504997 1
0.1%

Population
Real number (ℝ)

Distinct1800
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43847318
Minimum34321
Maximum1.36704 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:53.228492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum34321
5-th percentile403211
Q13103760
median7544290
Q323973100
95-th percentile1.44585 × 108
Maximum1.36704 × 109
Range1.3670057 × 109
Interquartile range (IQR)20869340

Descriptive statistics

Standard deviation1.5385416 × 108
Coefficient of variation (CV)3.5088615
Kurtosis38.744938
Mean43847318
Median Absolute Deviation (MAD)5679690
Skewness6.1176136
Sum7.8969019 × 1010
Variance2.3671104 × 1016
MonotonicityNot monotonic
2023-10-24T11:58:53.373662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16347200 2
 
0.1%
24119000 1
 
0.1%
16440200 1
 
0.1%
4331280 1
 
0.1%
4612230 1
 
0.1%
5171730 1
 
0.1%
3717350 1
 
0.1%
15563300 1
 
0.1%
15745600 1
 
0.1%
15926200 1
 
0.1%
Other values (1790) 1790
99.4%
ValueCountFrequency (%)
34321 1
0.1%
34596 1
0.1%
34852 1
0.1%
35095 1
0.1%
35322 1
0.1%
74213 1
0.1%
77675 1
0.1%
79188 1
0.1%
81151 1
0.1%
84203 1
0.1%
ValueCountFrequency (%)
1367040000 1
0.1%
1359290000 1
0.1%
1320080000 1
0.1%
1277980000 1
0.1%
1215910000 1
0.1%
1198880000 1
0.1%
1179690000 1
0.1%
1179680000 1
0.1%
1158670000 1
0.1%
1136800000 1
0.1%
Distinct65
Distinct (%)85.5%
Missing1725
Missing (%)95.8%
Infinite0
Infinite (%)0.0%
Mean2.6989484
Minimum1.4
Maximum4.29032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:53.543434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile1.7756425
Q12.14872
median2.53125
Q33.1282375
95-th percentile4.0521875
Maximum4.29032
Range2.89032
Interquartile range (IQR)0.9795175

Descriptive statistics

Standard deviation0.71973404
Coefficient of variation (CV)0.26667202
Kurtosis-0.5769846
Mean2.6989484
Median Absolute Deviation (MAD)0.46154
Skewness0.54077613
Sum205.12008
Variance0.51801708
MonotonicityNot monotonic
2023-10-24T11:58:53.684020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 4
 
0.2%
2.5 2
 
0.1%
2.33333 2
 
0.1%
2.83333 2
 
0.1%
2.25 2
 
0.1%
3.82353 2
 
0.1%
2.3 2
 
0.1%
2.125 2
 
0.1%
2.28571 2
 
0.1%
3.51064 1
 
0.1%
Other values (55) 55
 
3.1%
(Missing) 1725
95.8%
ValueCountFrequency (%)
1.4 1
0.1%
1.53333 1
0.1%
1.6875 1
0.1%
1.76923 1
0.1%
1.77778 1
0.1%
1.78261 1
0.1%
1.83333 1
0.1%
1.88889 1
0.1%
1.90909 1
0.1%
1.91667 1
0.1%
ValueCountFrequency (%)
4.29032 1
0.1%
4.13333 1
0.1%
4.11321 1
0.1%
4.06061 1
0.1%
4.04938 1
0.1%
4 1
0.1%
3.94898 1
0.1%
3.82353 2
0.1%
3.82143 1
0.1%
3.80952 1
0.1%

Gini coefficient
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct408
Distinct (%)100.0%
Missing1393
Missing (%)77.3%
Infinite0
Infinite (%)0.0%
Mean0.3776982
Minimum0.21603719
Maximum0.64731308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:53.815969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.21603719
5-th percentile0.26898248
Q10.30881086
median0.3516391
Q30.43781966
95-th percentile0.54893146
Maximum0.64731308
Range0.43127589
Interquartile range (IQR)0.1290088

Descriptive statistics

Standard deviation0.090079597
Coefficient of variation (CV)0.23849623
Kurtosis-0.28983698
Mean0.3776982
Median Absolute Deviation (MAD)0.053667011
Skewness0.78726274
Sum154.10087
Variance0.0081143338
MonotonicityNot monotonic
2023-10-24T11:58:53.957014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3004332658 1
 
0.1%
0.2762729373 1
 
0.1%
0.3157907692 1
 
0.1%
0.3059755725 1
 
0.1%
0.263541735 1
 
0.1%
0.2709081418 1
 
0.1%
0.3609754303 1
 
0.1%
0.4442544419 1
 
0.1%
0.3730445423 1
 
0.1%
0.2980002607 1
 
0.1%
Other values (398) 398
 
22.1%
(Missing) 1393
77.3%
ValueCountFrequency (%)
0.2160371934 1
0.1%
0.2376631971 1
0.1%
0.2432197027 1
0.1%
0.2435475319 1
0.1%
0.2436392682 1
0.1%
0.2463486319 1
0.1%
0.2479073845 1
0.1%
0.2491279652 1
0.1%
0.2518688906 1
0.1%
0.2526974636 1
0.1%
ValueCountFrequency (%)
0.6473130798 1
0.1%
0.6331875979 1
0.1%
0.6324036513 1
0.1%
0.613307461 1
0.1%
0.5873960008 1
0.1%
0.5868289301 1
0.1%
0.5825690382 1
0.1%
0.5811364313 1
0.1%
0.5756279188 1
0.1%
0.5727629008 1
0.1%

Total Fertilizer Use
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct478
Distinct (%)92.5%
Missing1284
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean101.45667
Minimum0
Maximum638.91
Zeros38
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:54.083131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.76
median66.49
Q3136.46
95-th percentile342.406
Maximum638.91
Range638.91
Interquartile range (IQR)125.7

Descriptive statistics

Standard deviation118.42439
Coefficient of variation (CV)1.167241
Kurtosis3.389216
Mean101.45667
Median Absolute Deviation (MAD)59.14
Skewness1.7743841
Sum52453.1
Variance14024.336
MonotonicityNot monotonic
2023-10-24T11:58:54.213025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
2.1%
15.97 2
 
0.1%
2.75 2
 
0.1%
4.49 1
 
0.1%
423.34 1
 
0.1%
3.82 1
 
0.1%
3.83 1
 
0.1%
6.11 1
 
0.1%
0.29 1
 
0.1%
0.24 1
 
0.1%
Other values (468) 468
 
26.0%
(Missing) 1284
71.3%
ValueCountFrequency (%)
0 38
2.1%
0.01 1
 
0.1%
0.05 1
 
0.1%
0.24 1
 
0.1%
0.29 1
 
0.1%
0.36 1
 
0.1%
0.38 1
 
0.1%
0.42 1
 
0.1%
0.53 1
 
0.1%
0.71 1
 
0.1%
ValueCountFrequency (%)
638.91 1
0.1%
616.56 1
0.1%
596.01 1
0.1%
588.35 1
0.1%
571.92 1
0.1%
535.21 1
0.1%
532.87 1
0.1%
477.39 1
0.1%
465.66 1
0.1%
456.88 1
0.1%

Agriculture Research Spending
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct219
Distinct (%)91.6%
Missing1562
Missing (%)86.7%
Infinite0
Infinite (%)0.0%
Mean249.73891
Minimum1.3
Maximum4976.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:54.359936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile3.88
Q116.3
median33.9
Q3202.65
95-th percentile1675.22
Maximum4976.1
Range4974.8
Interquartile range (IQR)186.35

Descriptive statistics

Standard deviation598.3624
Coefficient of variation (CV)2.3959518
Kurtosis26.005382
Mean249.73891
Median Absolute Deviation (MAD)27.7
Skewness4.6255292
Sum59687.6
Variance358037.56
MonotonicityNot monotonic
2023-10-24T11:58:54.511901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
0.2%
22.1 3
 
0.2%
18.7 2
 
0.1%
11.9 2
 
0.1%
11 2
 
0.1%
19.7 2
 
0.1%
10.2 2
 
0.1%
15.4 2
 
0.1%
72.8 2
 
0.1%
17.7 2
 
0.1%
Other values (209) 217
 
12.0%
(Missing) 1562
86.7%
ValueCountFrequency (%)
1.3 1
 
0.1%
2 3
0.2%
2.2 1
 
0.1%
2.6 1
 
0.1%
2.8 1
 
0.1%
2.9 2
0.1%
3.1 1
 
0.1%
3.4 1
 
0.1%
3.7 1
 
0.1%
3.9 1
 
0.1%
ValueCountFrequency (%)
4976.1 1
0.1%
4077.9 1
0.1%
2767.3 1
0.1%
2703.8 1
0.1%
2169.6 1
0.1%
2164.1 1
0.1%
2095.9 1
0.1%
2092.4 1
0.1%
1739.1 1
0.1%
1729.3 1
0.1%

GHI
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1686
Missing (%)93.6%
Memory size71.1 KiB

Gross_Prod_Per_Capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1801
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.91644
Minimum23.165542
Maximum1753.9443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2023-10-24T11:58:54.652758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum23.165542
5-th percentile67.652159
Q1119.43562
median157.78599
Q3245.73112
95-th percentile564.6206
Maximum1753.9443
Range1730.7788
Interquartile range (IQR)126.2955

Descriptive statistics

Standard deviation158.05823
Coefficient of variation (CV)0.75295786
Kurtosis10.900391
Mean209.91644
Median Absolute Deviation (MAD)51.394729
Skewness2.6760045
Sum378059.51
Variance24982.406
MonotonicityNot monotonic
2023-10-24T11:58:54.784091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92.30672913 1
 
0.1%
138.3003921 1
 
0.1%
121.4987255 1
 
0.1%
123.1321942 1
 
0.1%
157.8462139 1
 
0.1%
1753.944342 1
 
0.1%
679.8166199 1
 
0.1%
622.6289249 1
 
0.1%
648.1878917 1
 
0.1%
604.1704737 1
 
0.1%
Other values (1791) 1791
99.4%
ValueCountFrequency (%)
23.16554168 1
0.1%
23.33125351 1
0.1%
24.96233508 1
0.1%
26.45506811 1
0.1%
29.92894668 1
0.1%
31.93195407 1
0.1%
32.10720846 1
0.1%
33.8273787 1
0.1%
36.08739523 1
0.1%
36.31706399 1
0.1%
ValueCountFrequency (%)
1753.944342 1
0.1%
1037.893573 1
0.1%
1023.466619 1
0.1%
1022.689884 1
0.1%
1017.196353 1
0.1%
1016.902079 1
0.1%
1004.416993 1
0.1%
1000.823897 1
0.1%
1000.696961 1
0.1%
996.1321015 1
0.1%

Interactions

2023-10-24T11:58:45.279752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:57.465678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:00.413495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.213897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:05.092973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:07.803120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:12.425415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:15.412006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:18.037009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:20.889475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.486293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:25.977288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:28.520939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.334250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.667847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.158860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:40.752743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.019086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:45.447771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:57.647048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:00.566730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.334090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:05.415523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:08.291651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:12.603266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:15.569933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:18.193760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:21.065892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.611657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:26.118784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:28.641335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.441371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.791751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.271953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:40.862880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.191438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:45.573376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:57.812579image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:00.735145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.450080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:05.564476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:08.848669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:12.839934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:15.711302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:18.332064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:21.202319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.746286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:26.267671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:28.772327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.609147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.917193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.378830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:40.978988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.309874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:45.698753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:57.982560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:00.898313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.582048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:05.690436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:09.286676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:12.987102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:15.837403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:18.461126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:21.324762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.863552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:26.397647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:28.898019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.728802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.058660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.473034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.116181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.430957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:45.849392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:58.132403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:01.049795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.702240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:05.800273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:09.456961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:13.144217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:15.988321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:18.602286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:21.459497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.984860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:26.529463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:29.053163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.852788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.185116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.582873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.230191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.541192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:45.990946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:58.297572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:01.380006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.816298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:05.972191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:09.695045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:13.474703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:16.166952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:18.790940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:21.595219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:24.105032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:26.654289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:29.176293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.976954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.315489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.708501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.375257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.647685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:46.132674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:58.459351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:01.549896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.914255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:06.097617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:09.899686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:13.614605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:16.292652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:18.938762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:21.711069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:24.257272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:26.788512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:29.301560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:32.106713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.432994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.803127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.478965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.739979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:46.267791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:58.612234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:01.718240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.014149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:06.245709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:10.088816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:13.734340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:16.447298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:19.062815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:21.852599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:24.388363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:26.925269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:29.475185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:32.258383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.557234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:38.916466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.604562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.843041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:46.396529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:58.783036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:01.882778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.114078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:06.363881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:10.422457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:13.867677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:16.574529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:19.192375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:22.009208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:24.517633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:27.082024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:29.604070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:32.446237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.713941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:39.030902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.731968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:43.963803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:46.522005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:58.934720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:02.048741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.208149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:06.492188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:10.595103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:13.977426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:16.726290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:19.298860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:22.140368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:24.653694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:27.249448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:30.043584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:32.584906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.825553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:39.147559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.855081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:44.062116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:46.647345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:59.082725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:02.194836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.302843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:06.634849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:10.832705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:14.103508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:16.868507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:19.432241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:22.298303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:24.790498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:27.366744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:30.216059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:32.696330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:34.953660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:39.255615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:41.966934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:44.175820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:46.784486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:59.232217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:02.337895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.401116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:06.760219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:11.026927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:14.276837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:17.030097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:19.572749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:22.429983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:24.901900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:27.477834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:30.342959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:32.810221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:35.081877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:39.355911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:42.092733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:44.302507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:46.888872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:59.383634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:02.465219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.491716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:06.934282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:11.215473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:14.448133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:17.165415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:19.698479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:22.602278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:25.007127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:27.610019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:30.467350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:32.926317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:35.199113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:39.456063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:42.217127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:44.411962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:47.003836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:59.534191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:02.598098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.572907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:07.047266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:11.373144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:14.590660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:17.275276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:19.832685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:22.724110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:25.170285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:27.707735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:30.576952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.052338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:35.324903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:39.556311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:42.333620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:44.553354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:47.239151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:59.834444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:02.848432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.762536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:07.293048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:11.736032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:14.873683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:17.531577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:20.071246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:22.983952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:25.484556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:27.962379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:30.847250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.292684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:35.561019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:40.299873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:42.569611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:44.784909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:47.358832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:57:59.967633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:02.946304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.838387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:07.392564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:11.893904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:14.972699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:17.639934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:20.196865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.094186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:25.603138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:28.108744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:30.981886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.379790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:35.659891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:40.409621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:42.695121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:44.909174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:47.464066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:00.116974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.040557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:04.936791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:07.486804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:12.066627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:15.129828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:17.794074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:20.369863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.251183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:25.708344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:28.218629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.073607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.474637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:35.785570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:40.519574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:42.825372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:45.023952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:47.572702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:00.250393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:03.119129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:05.014863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:07.597260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:12.224917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:15.254810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:17.915083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:20.499745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:23.360930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:25.855887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:28.375905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:31.200269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:33.560811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:35.895287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:40.645343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:42.920029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-10-24T11:58:45.152382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-10-24T11:58:54.928399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
YearGross_ProductionGDPGDP_Per_CapitaNo of frost daysPrecipitationAvg temperatureControl of Corruption: EstimateUse of IMF credit (DOD, current US$)Gross enrolment ratio, primary to tertiary, both sexes (%)Gross enrolment ratio, primary to tertiary, female (%)Gross enrolment ratio, primary to tertiary, male (%)AreaPopulationQuality of trade- and transport-related infrastructure, score (1=low to 5=high)Gini coefficientTotal Fertilizer UseAgriculture Research SpendingGross_Prod_Per_Capita
Year1.0000.1140.4030.5680.264-0.186-0.184-0.0560.4460.5760.5850.549-0.0830.033NaN-0.1890.066-0.0110.246
Gross_Production0.1141.0000.7890.0920.3100.008-0.278-0.0670.4860.2070.1850.2300.6940.9390.305-0.0980.3130.8210.401
GDP0.4030.7891.0000.6010.412-0.123-0.3750.3930.5300.5870.5840.6080.4310.6690.753-0.2610.6300.8070.473
GDP_Per_Capita0.5680.0920.6011.0000.328-0.151-0.3420.8270.2150.8280.8520.827-0.178-0.1170.813-0.3340.6550.3000.577
No of frost days0.2640.3100.4120.3281.000-0.354-0.8330.2960.2520.4900.5080.5230.1460.1680.331-0.5420.2910.3210.392
Precipitation-0.1860.008-0.123-0.151-0.3541.0000.159-0.042-0.134-0.059-0.041-0.058-0.1940.011-0.0720.3000.074-0.0560.010
Avg temperature-0.184-0.278-0.375-0.342-0.8330.1591.000-0.400-0.140-0.522-0.558-0.562-0.025-0.112-0.4090.559-0.402-0.155-0.493
Control of Corruption: Estimate-0.056-0.0670.3930.8270.296-0.042-0.4001.000-0.1970.7060.7110.696-0.351-0.2840.732-0.4260.5720.1290.449
Use of IMF credit (DOD, current US$)0.4460.4860.5300.2150.252-0.134-0.140-0.1971.0000.3180.2630.2660.3500.4970.384-0.3410.2970.6930.093
Gross enrolment ratio, primary to tertiary, both sexes (%)0.5760.2070.5870.8280.490-0.059-0.5220.7060.3181.0000.9920.982-0.1510.0080.719-0.3890.6110.1730.568
Gross enrolment ratio, primary to tertiary, female (%)0.5850.1850.5840.8520.508-0.041-0.5580.7110.2630.9921.0000.952-0.200-0.0330.707-0.4440.5760.2030.612
Gross enrolment ratio, primary to tertiary, male (%)0.5490.2300.6080.8270.523-0.058-0.5620.6960.2660.9820.9521.000-0.1610.0220.725-0.4240.6270.1480.603
Area-0.0830.6940.431-0.1780.146-0.194-0.025-0.3510.350-0.151-0.200-0.1611.0000.746-0.0500.180-0.1070.6370.057
Population0.0330.9390.669-0.1170.1680.011-0.112-0.2840.4970.008-0.0330.0220.7461.0000.0710.0370.1130.7540.114
Quality of trade- and transport-related infrastructure, score (1=low to 5=high)NaN0.3050.7530.8130.331-0.072-0.4090.7320.3840.7190.7070.725-0.0500.0711.000-0.3660.6580.5780.548
Gini coefficient-0.189-0.098-0.261-0.334-0.5420.3000.559-0.426-0.341-0.389-0.444-0.4240.1800.037-0.3661.000-0.288-0.063-0.360
Total Fertilizer Use0.0660.3130.6300.6550.2910.074-0.4020.5720.2970.6110.5760.627-0.1070.1130.658-0.2881.0000.4560.565
Agriculture Research Spending-0.0110.8210.8070.3000.321-0.056-0.1550.1290.6930.1730.2030.1480.6370.7540.578-0.0630.4561.0000.434
Gross_Prod_Per_Capita0.2460.4010.4730.5770.3920.010-0.4930.4490.0930.5680.6120.6030.0570.1140.548-0.3600.5650.4341.000

Missing values

2023-10-24T11:58:47.764649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-24T11:58:48.097197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-24T11:58:48.385366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearGross_ProductionGDPGDP_Per_CapitaCountry_CodeNo of frost daysPrecipitationAvg temperatureControl of Corruption: EstimateUse of IMF credit (DOD, current US$)Gross enrolment ratio, primary to tertiary, both sexes (%)Gross enrolment ratio, primary to tertiary, female (%)Gross enrolment ratio, primary to tertiary, male (%)AreaPopulationQuality of trade- and transport-related infrastructure, score (1=low to 5=high)Gini coefficientTotal Fertilizer UseAgriculture Research SpendingGHIGross_Prod_Per_Capita
0Afghanistan20042226346.05.226779e+09211.382074AFG8.2726.6213.19-1.350647NaN50.9241729.0986571.2686238064.024119000.0NaNNaN4.49NaNNaN92.306729
1Afghanistan20032289434.04.515559e+09190.683814AFG9.2729.1512.47-1.344180NaN44.5998831.1166357.1223738899.023064900.0NaNNaN3.22NaNNaN99.260521
2Albania2007824818.01.067732e+103595.038057ALB4.3690.0412.94-0.706940163478853.770.7096171.5302069.928521119.03023910.02.33333NaN72.18NaN15.8272.765393
3Albania2006858366.08.896073e+092972.742924ALB5.8390.8211.85-0.790545162459042.768.9378568.9511168.925061120.03054330.0NaNNaN70.49NaNNaN281.032501
4Albania2005813707.08.052074e+092673.786584ALB6.11122.8611.61-0.813264158243429.068.6611968.4626568.853401077.03079180.0NaN0.30595791.11NaNNaN264.260940
5Albania2004819870.07.184686e+092373.581292ALB4.97124.4812.20-0.723732169099897.666.2148366.0947866.331121122.03097750.0NaNNaN83.18NaNNaN264.666290
6Albania2003789269.05.611496e+091846.120121ALB5.5598.5012.75-0.853787159415248.066.52603NaNNaN1121.03111010.0NaNNaN81.80NaNNaN253.701852
7Albania2002755188.04.348068e+091425.124219ALB3.91104.9112.60-0.845341144218235.165.96581NaNNaN1140.03119030.0NaN0.31739080.36NaNNaN242.122711
8Albania2001747971.03.922101e+091281.659826ALB4.8596.1912.54NaN141638824.666.3658566.1895166.538721139.03122410.0NaNNaNNaNNaNNaN239.549258
9Albania2000733849.03.480355e+091126.683340ALB5.5881.5612.56-0.855564148425240.265.9646165.4222466.495421144.03121970.0NaNNaNNaNNaN20.7235.059594
CountryYearGross_ProductionGDPGDP_Per_CapitaCountry_CodeNo of frost daysPrecipitationAvg temperatureControl of Corruption: EstimateUse of IMF credit (DOD, current US$)Gross enrolment ratio, primary to tertiary, both sexes (%)Gross enrolment ratio, primary to tertiary, female (%)Gross enrolment ratio, primary to tertiary, male (%)AreaPopulationQuality of trade- and transport-related infrastructure, score (1=low to 5=high)Gini coefficientTotal Fertilizer UseAgriculture Research SpendingGHIGross_Prod_Per_Capita
1791Zimbabwe19801096754.06.678868e+09901.498415ZWE0.057.5020.75NaN0.041.81685NaNNaN12275.07164170.0NaNNaNNaNNaNNaN153.088774
1792Zimbabwe19791066642.05.177459e+09723.106734ZWE0.050.4220.60NaN0.029.99928NaNNaN12215.06921790.0NaNNaNNaNNaNNaN154.099156
1793Zimbabwe19781133310.04.351600e+09627.967118ZWE0.069.8820.26NaN0.031.09332NaNNaN12165.06703180.0NaNNaNNaNNaNNaN169.070501
1794Zimbabwe19771136339.04.364382e+09650.155799ZWE0.079.0220.80NaN0.034.40660NaNNaN12115.06501890.0NaNNaNNaNNaNNaN174.770567
1795Zimbabwe19761190166.04.318372e+09664.102756ZWE0.068.9220.05NaN0.035.17220NaNNaN12090.06308300.0NaNNaNNaNNaNNaN188.666677
1796Zimbabwe19751075245.04.371301e+09694.532494ZWE0.060.9520.21NaN0.035.55243NaNNaN12055.06115370.0NaNNaNNaNNaNNaN175.826647
1797Zimbabwe19741130246.03.982161e+09654.414192ZWE0.069.7619.90NaN0.035.67601NaNNaN12020.05920940.0NaNNaNNaNNaNNaN190.889622
1798Zimbabwe1973939421.03.309354e+09563.033078ZWE0.059.8520.94NaN0.035.31014NaNNaN11935.05727040.0NaNNaNNaNNaNNaN164.032554
1799Zimbabwe19721117550.02.677729e+09471.936903ZWE0.056.7320.57NaN0.035.27831NaNNaN11900.05535870.0NaNNaNNaNNaNNaN201.874321
1800Zimbabwe1971988307.02.178716e+09397.795335ZWE0.049.4321.06NaN0.033.95027NaNNaN11835.05351200.0NaNNaNNaNNaNNaN184.688855